Digital Health: OptiMuscle - Improving health outcomes through the optimization of muscle function
Lead Research Organisation:
University of Salford
Department Name: School of Health and Society
Abstract
Approximately 10% of people in the UK population exhibit some form of dysfunctional breathing. This term describes a range of conditions which are characterised by an impairment in the muscular control of breathing and which can result in breathlessness, hyperventilation and in some cases dizziness. Current clinical assessment techniques and treatments for dysfunctional breathing are low-tech, with clinical management focused around the use of simple breathing exercises. Whilst these exercises do have some effect, we propose that patients would get more benefit from a system which uses biofeedback on muscle patterns to guide breathing re-education. Furthermore, if such a system could be automated, it should be possible to provide breathing re-education to large numbers of people without the need for a clinical specialist to be present. This is important because breathing re-education is often a key part of the clinical management of other health conditions, such as asthma, back pain and anxiety.
This project will deliver a completely new digital health system for the clinical management of dysfunctional breathing. The system will use both visual and auditory biofeedback to communicate abnormal muscle function, guiding patients through a process in which they gradually learn the correct muscular control of breathing. To realise this personalised system, we will develop software which can create an individual avatar of the patient and use this to visualise the actions of the breathing muscle in real-time. To avoid the need to directly measure muscles in a clinical setting, we will develop algorithms which can predict muscle activations from an input of simple sensor data which can be collected in a clinical setting, e.g. inexpensive 3D camera.
To complement the use of visual biofeedback, we will use auditory biofeedback to convey subtle changes in muscle function and help reinforce the learning of new muscle patterns. Our multimodal (visual and audio) biofeedback system will be integrated into a behaviour change intervention, providing patients with the capability, opportunity and motivation to change their muscle-related breathing behaviours. We will develop our new intervention by working closely with patients to understand their views on how the final system should operate. Once created, we will carry out a small trial on people with dysfunctional breathing to understand the future potential. If this testing provides encouraging results, then we will apply for funding for a larger NHS trial.
This project will deliver a completely new digital health system for the clinical management of dysfunctional breathing. The system will use both visual and auditory biofeedback to communicate abnormal muscle function, guiding patients through a process in which they gradually learn the correct muscular control of breathing. To realise this personalised system, we will develop software which can create an individual avatar of the patient and use this to visualise the actions of the breathing muscle in real-time. To avoid the need to directly measure muscles in a clinical setting, we will develop algorithms which can predict muscle activations from an input of simple sensor data which can be collected in a clinical setting, e.g. inexpensive 3D camera.
To complement the use of visual biofeedback, we will use auditory biofeedback to convey subtle changes in muscle function and help reinforce the learning of new muscle patterns. Our multimodal (visual and audio) biofeedback system will be integrated into a behaviour change intervention, providing patients with the capability, opportunity and motivation to change their muscle-related breathing behaviours. We will develop our new intervention by working closely with patients to understand their views on how the final system should operate. Once created, we will carry out a small trial on people with dysfunctional breathing to understand the future potential. If this testing provides encouraging results, then we will apply for funding for a larger NHS trial.
| Description | Achievement 1: Precise quantification of respiratory movements and respiratory muscle control in people who have dysfunctional breathing. We have created a new analysis technique which allows us to track motions of the diaphragm during breathing using 3D ultrasound. We have also used optoelectronic plethysmography to measure precise 3D shape changes in the torso during breathing and used electromyography to measure muscle activations during breathing. To date, we have collected data on 25 people who have dysfunctional breathing and 15 healthy people. Once analysed, these data will allow us to describe alterations in the muscular and mechanical control of breathing which are associated with dysfunctional breathing. These data will also be used to develop our muscle prediction algorithm (see below). Achievement 2: New technique for assessing breathing volumes with 3D cameras. Recent advances in camera technology mean it is possible take 3D measurements across an image to mm level accuracy. These cameras are relatively inexpensive (£200-£400) and have the potential to be used to assess breathing function. Using a system of two 3D cameras, we have created a measurement system which can estimate breathing volumes (air flow in/out of the lungs) to a level of accuracy which is similar to a spirometer (breathing mask). This system has the potential to be developed into a cheap diagnostic system to assess breathing pattern disorder in clinical practice. Achievement 3: Real-time biofeedback of the mechanics of breathing. We have created software which takes as input data from two 3D cameras and visualises the corresponding mechanics of breathing on a personalised avatar, e.g. diaphragmatic movements, rib motions, shoulder motions. In addition, using a muscle prediction algorithm, the system can also infer the underlying pattern of muscle control. These muscle patterns are also visualised on the avatar. We are current testing this system on patients and are confident that it will respond to subtle changes in breathing in real-time, allowing the user to easily understand whether they have been able to improve their muscle control of breathing. This breathing visualisation software was developed through extensive user consultation with a group of people who have dysfunctional breathing. Moving forwards, we are looking to integrate this breathing biofeedback into new treatments for conditions associated with breathlessness, such as long-COVID, asthma and COPD. Achievement 4: Exploration of illness perceptions in people with breathing pattern disorder. Alongside our technical development, we performed a survey with 82 people who have a clinical diagnosis of breathing pattern disorder. This survey showed that illness perceptions are generally negative with low coherent understanding and beliefs that the illness is chronic, difficult to control, has substantial symptoms. Individuals with breathing pattern disorder also experience poor mental health with high scores of depression and anxiety which is greater for those who also perceive their illness more negatively. This work highlights the importance of developing new treatment approaches for breathing pattern disorder and will feed into our subsequent intervention development work. |
| Exploitation Route | We envisage our technique for tracking ultrasound motions of the diaphragm to be used by researchers to quantify abnormalities in diaphragmatic control across different respiratory conditions. We also envisage that our approach for quantifying respiratory volumes will be used by researchers as an alternative to current optoelectronic plethysmography systems. To date, there is no objective method for diagnosing/characterising breathing pattern disorder. With further development of our measurement system and software, we are confident that we can develop a fully automated diagnostic system. This would be deployed in healthcare settings, e.g. GP practices, to screen for breathing problems. We anticipate further NIHR funding to realise this vision. We are confident that our breathing biofeedback system can be integrated into healthcare interventions which are aimed at improving the muscle control of breathing. We have already been awarded one small grant to create a new intervention for people with long-COVID by combining our breathing biofeedback with Cognitive Muscular Therapy®, an intervention developed at the University of Salford. Moving forwards, we anticipate NIHR funding to create interventions for other conditions, such as asthma and COPD. We also anticipate industry backing to create a system which could be used in the health and wellbeing sector to help people to improve breathing control during exercise. |
| Sectors | Healthcare |
| Description | A new intervention for people with long-COVID |
| Amount | £25,000 (GBP) |
| Organisation | The Dowager Countess Eleanor Peel Trust |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 01/2024 |
| End | 05/2025 |
| Title | 3D ultrasound to track the diaphragm |
| Description | Use computational method which can automatically track 3D motions of the diaphragm during breathing. |
| Type Of Material | Data analysis technique |
| Year Produced | 2024 |
| Provided To Others? | No |
| Impact | We have submitted this approach for publication. Once published, it will be made available for use. |
| Title | Real-time system to visualise the mechanics of human breathing |
| Description | This software uses input from two inexpensive 3D cameras and generates a real-time visualisation of the mechanics of human breathing, including rib motions, diaphragm motions etc. |
| Type Of Technology | Software |
| Year Produced | 2024 |
| Impact | No impacts yet. We are exploring this at the moment |